Near-Optimal Ambiguity sets for Distributionally Robust Optimization
We propose a novel, Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO). The key idea is to measure the relative size between a candidate ambiguity set and an \emph{asymptotically optimal} set as the amount of data grows large. This asymptotically optimal set is provably the smallest convex … Read more